Overview

Dataset statistics

Number of variables13
Number of observations907
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory190.9 KiB
Average record size in memory215.6 B

Variable types

Text1
Numeric9
Categorical3

Alerts

AveRPO is highly overall correlated with AveRPW and 2 other fieldsHigh correlation
AveRPW is highly overall correlated with AveRPO and 3 other fieldsHigh correlation
HS is highly overall correlated with AveRPO and 2 other fieldsHigh correlation
Inns is highly overall correlated with Lost and 2 other fieldsHigh correlation
Lost is highly overall correlated with Inns and 1 other fieldsHigh correlation
Mat is highly overall correlated with Inns and 2 other fieldsHigh correlation
W/L is highly overall correlated with AveRPO and 2 other fieldsHigh correlation
Won is highly overall correlated with AveRPW and 4 other fieldsHigh correlation
Tied is highly imbalanced (78.4%)Imbalance
NR is highly imbalanced (68.1%)Imbalance
Won has 142 (15.7%) zerosZeros
Lost has 84 (9.3%) zerosZeros
W/L has 142 (15.7%) zerosZeros
LS has 322 (35.5%) zerosZeros

Reproduction

Analysis started2024-11-06 14:20:57.811320
Analysis finished2024-11-06 14:21:31.890634
Duration34.08 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Team
Text

Distinct105
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
2024-11-06T19:51:32.455302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length7.9140022
Min length4

Characters and Unicode

Total characters7178
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.7%

Sample

1st rowZimbabwe
2nd rowZimbabwe
3rd rowZimbabwe
4th rowZimbabwe
5th rowZimbabwe
ValueCountFrequency (%)
england 37
 
3.4%
sri 34
 
3.1%
lanka 34
 
3.1%
west 34
 
3.1%
indies 34
 
3.1%
australia 34
 
3.1%
pakistan 34
 
3.1%
india 32
 
2.9%
new 32
 
2.9%
zealand 32
 
2.9%
Other values (110) 750
69.0%
2024-11-06T19:51:33.573321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1157
16.1%
n 649
 
9.0%
i 491
 
6.8%
e 487
 
6.8%
l 333
 
4.6%
r 316
 
4.4%
d 315
 
4.4%
t 292
 
4.1%
s 291
 
4.1%
o 189
 
2.6%
Other values (44) 2658
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1157
16.1%
n 649
 
9.0%
i 491
 
6.8%
e 487
 
6.8%
l 333
 
4.6%
r 316
 
4.4%
d 315
 
4.4%
t 292
 
4.1%
s 291
 
4.1%
o 189
 
2.6%
Other values (44) 2658
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1157
16.1%
n 649
 
9.0%
i 491
 
6.8%
e 487
 
6.8%
l 333
 
4.6%
r 316
 
4.4%
d 315
 
4.4%
t 292
 
4.1%
s 291
 
4.1%
o 189
 
2.6%
Other values (44) 2658
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1157
16.1%
n 649
 
9.0%
i 491
 
6.8%
e 487
 
6.8%
l 333
 
4.6%
r 316
 
4.4%
d 315
 
4.4%
t 292
 
4.1%
s 291
 
4.1%
o 189
 
2.6%
Other values (44) 2658
37.0%

Mat
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4762955
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:33.964131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile15
Maximum24
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2008409
Coefficient of variation (CV)0.64864874
Kurtosis1.3736498
Mean6.4762955
Median Absolute Deviation (MAD)2
Skewness1.1966795
Sum5874
Variance17.647064
MonotonicityNot monotonic
2024-11-06T19:51:34.368219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4 135
14.9%
5 110
12.1%
3 109
12.0%
6 86
9.5%
2 72
7.9%
7 58
6.4%
8 52
 
5.7%
10 48
 
5.3%
1 47
 
5.2%
9 46
 
5.1%
Other values (14) 144
15.9%
ValueCountFrequency (%)
1 47
 
5.2%
2 72
7.9%
3 109
12.0%
4 135
14.9%
5 110
12.1%
6 86
9.5%
7 58
6.4%
8 52
 
5.7%
9 46
 
5.1%
10 48
 
5.3%
ValueCountFrequency (%)
24 2
 
0.2%
23 1
 
0.1%
22 1
 
0.1%
21 4
 
0.4%
20 1
 
0.1%
19 2
 
0.2%
18 9
1.0%
17 8
0.9%
16 11
1.2%
15 14
1.5%

Won
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1201764
Minimum0
Maximum20
Zeros142
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:34.724328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.874087
Coefficient of variation (CV)0.92112965
Kurtosis2.7752291
Mean3.1201764
Median Absolute Deviation (MAD)2
Skewness1.424999
Sum2830
Variance8.2603761
MonotonicityNot monotonic
2024-11-06T19:51:35.107059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 179
19.7%
2 144
15.9%
0 142
15.7%
3 126
13.9%
4 86
9.5%
5 65
 
7.2%
6 59
 
6.5%
7 34
 
3.7%
9 23
 
2.5%
8 16
 
1.8%
Other values (8) 33
 
3.6%
ValueCountFrequency (%)
0 142
15.7%
1 179
19.7%
2 144
15.9%
3 126
13.9%
4 86
9.5%
5 65
 
7.2%
6 59
 
6.5%
7 34
 
3.7%
8 16
 
1.8%
9 23
 
2.5%
ValueCountFrequency (%)
20 1
 
0.1%
17 1
 
0.1%
15 1
 
0.1%
14 3
 
0.3%
13 1
 
0.1%
12 7
 
0.8%
11 8
 
0.9%
10 11
1.2%
9 23
2.5%
8 16
1.8%

Lost
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1201764
Minimum0
Maximum18
Zeros84
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:35.488370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile8
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4426989
Coefficient of variation (CV)0.78287205
Kurtosis3.6145194
Mean3.1201764
Median Absolute Deviation (MAD)2
Skewness1.4372061
Sum2830
Variance5.9667779
MonotonicityNot monotonic
2024-11-06T19:51:35.802661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 183
20.2%
1 164
18.1%
3 161
17.8%
4 103
11.4%
0 84
9.3%
5 81
8.9%
6 47
 
5.2%
7 35
 
3.9%
8 20
 
2.2%
9 12
 
1.3%
Other values (7) 17
 
1.9%
ValueCountFrequency (%)
0 84
9.3%
1 164
18.1%
2 183
20.2%
3 161
17.8%
4 103
11.4%
5 81
8.9%
6 47
 
5.2%
7 35
 
3.9%
8 20
 
2.2%
9 12
 
1.3%
ValueCountFrequency (%)
18 1
 
0.1%
17 1
 
0.1%
14 1
 
0.1%
13 1
 
0.1%
12 4
 
0.4%
11 5
 
0.6%
10 4
 
0.4%
9 12
 
1.3%
8 20
2.2%
7 35
3.9%

Tied
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
0
834 
1
 
69
2
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters907
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 834
92.0%
1 69
 
7.6%
2 3
 
0.3%
3 1
 
0.1%

Length

2024-11-06T19:51:36.147014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T19:51:36.675977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 834
92.0%
1 69
 
7.6%
2 3
 
0.3%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 834
92.0%
1 69
 
7.6%
2 3
 
0.3%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 834
92.0%
1 69
 
7.6%
2 3
 
0.3%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 834
92.0%
1 69
 
7.6%
2 3
 
0.3%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 834
92.0%
1 69
 
7.6%
2 3
 
0.3%
3 1
 
0.1%

NR
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
0
788 
1
106 
2
 
9
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters907
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 788
86.9%
1 106
 
11.7%
2 9
 
1.0%
3 4
 
0.4%

Length

2024-11-06T19:51:37.031243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T19:51:37.322163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 788
86.9%
1 106
 
11.7%
2 9
 
1.0%
3 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 788
86.9%
1 106
 
11.7%
2 9
 
1.0%
3 4
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 788
86.9%
1 106
 
11.7%
2 9
 
1.0%
3 4
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 788
86.9%
1 106
 
11.7%
2 9
 
1.0%
3 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 788
86.9%
1 106
 
11.7%
2 9
 
1.0%
3 4
 
0.4%

W/L
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4636471
Minimum0
Maximum14
Zeros142
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:37.661847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.33333333
median1
Q32
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)1.6666667

Descriptive statistics

Standard deviation1.8310103
Coefficient of variation (CV)1.2509917
Kurtosis11.523731
Mean1.4636471
Median Absolute Deviation (MAD)0.66666667
Skewness2.8691036
Sum1327.5279
Variance3.3525987
MonotonicityNot monotonic
2024-11-06T19:51:38.069747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 145
16.0%
0 142
15.7%
2 70
 
7.7%
0.5 64
 
7.1%
3 48
 
5.3%
0.3333333333 41
 
4.5%
0.6666666667 40
 
4.4%
1.5 38
 
4.2%
4 30
 
3.3%
0.25 19
 
2.1%
Other values (63) 270
29.8%
ValueCountFrequency (%)
0 142
15.7%
0.1111111111 1
 
0.1%
0.125 2
 
0.2%
0.1428571429 2
 
0.2%
0.1666666667 6
 
0.7%
0.1818181818 1
 
0.1%
0.2 15
 
1.7%
0.25 19
 
2.1%
0.2857142857 4
 
0.4%
0.3 1
 
0.1%
ValueCountFrequency (%)
14 3
 
0.3%
11 2
 
0.2%
10 3
 
0.3%
9 6
 
0.7%
8 2
 
0.2%
7 6
 
0.7%
6 13
1.4%
5.5 2
 
0.2%
5 19
2.1%
4.5 1
 
0.1%

Inns
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4068357
Minimum0
Maximum24
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:38.453991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile15
Maximum24
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1692766
Coefficient of variation (CV)0.65075441
Kurtosis1.3503549
Mean6.4068357
Median Absolute Deviation (MAD)2
Skewness1.1949073
Sum5811
Variance17.382867
MonotonicityNot monotonic
2024-11-06T19:51:38.817838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 131
14.4%
5 113
12.5%
3 111
12.2%
6 92
10.1%
2 74
8.2%
7 52
 
5.7%
8 50
 
5.5%
10 49
 
5.4%
1 48
 
5.3%
9 47
 
5.2%
Other values (15) 140
15.4%
ValueCountFrequency (%)
0 1
 
0.1%
1 48
 
5.3%
2 74
8.2%
3 111
12.2%
4 131
14.4%
5 113
12.5%
6 92
10.1%
7 52
 
5.7%
8 50
 
5.5%
9 47
 
5.2%
ValueCountFrequency (%)
24 1
 
0.1%
23 2
 
0.2%
22 1
 
0.1%
21 2
 
0.2%
20 3
 
0.3%
19 1
 
0.1%
18 10
1.1%
17 7
0.8%
16 10
1.1%
15 16
1.8%

HS
Real number (ℝ)

HIGH CORRELATION 

Distinct183
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.01103
Minimum0
Maximum344
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:39.194578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105
Q1148
median175
Q3198
95-th percentile236
Maximum344
Range344
Interquartile range (IQR)50

Descriptive statistics

Standard deviation39.316145
Coefficient of variation (CV)0.22724647
Kurtosis0.99871555
Mean173.01103
Median Absolute Deviation (MAD)25
Skewness-0.20053669
Sum156921
Variance1545.7593
MonotonicityNot monotonic
2024-11-06T19:51:39.615487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176 16
 
1.8%
180 16
 
1.8%
185 15
 
1.7%
175 13
 
1.4%
191 13
 
1.4%
174 13
 
1.4%
166 13
 
1.4%
183 13
 
1.4%
141 13
 
1.4%
158 12
 
1.3%
Other values (173) 770
84.9%
ValueCountFrequency (%)
0 1
0.1%
31 1
0.1%
53 1
0.1%
57 1
0.1%
58 1
0.1%
60 1
0.1%
68 1
0.1%
72 2
0.2%
73 1
0.1%
74 1
0.1%
ValueCountFrequency (%)
344 1
 
0.1%
314 1
 
0.1%
297 1
 
0.1%
278 2
0.2%
268 1
 
0.1%
267 1
 
0.1%
263 1
 
0.1%
260 2
0.2%
259 1
 
0.1%
258 3
0.3%

LS
Real number (ℝ)

ZEROS 

Distinct148
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.244763
Minimum0
Maximum200
Zeros322
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:40.050005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median76
Q3109
95-th percentile145.7
Maximum200
Range200
Interquartile range (IQR)109

Descriptive statistics

Standard deviation54.467679
Coefficient of variation (CV)0.84781509
Kurtosis-1.3426165
Mean64.244763
Median Absolute Deviation (MAD)50
Skewness0.051318377
Sum58270
Variance2966.7281
MonotonicityNot monotonic
2024-11-06T19:51:40.532077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 322
35.5%
101 13
 
1.4%
95 11
 
1.2%
127 10
 
1.1%
111 10
 
1.1%
97 10
 
1.1%
89 10
 
1.1%
80 9
 
1.0%
114 9
 
1.0%
126 8
 
0.9%
Other values (138) 495
54.6%
ValueCountFrequency (%)
0 322
35.5%
10 2
 
0.2%
18 1
 
0.1%
21 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 4
 
0.4%
33 1
 
0.1%
ValueCountFrequency (%)
200 1
0.1%
193 1
0.1%
192 1
0.1%
187 1
0.1%
185 1
0.1%
178 1
0.1%
174 1
0.1%
173 2
0.2%
172 1
0.1%
171 1
0.1%

Season
Categorical

Distinct41
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size55.5 KiB
2024
83 
2022
72 
2023
65 
2023/24
61 
2021/22
57 
Other values (36)
569 

Length

Max length7
Median length7
Mean length5.508269
Min length4

Characters and Unicode

Total characters4996
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024/25
2nd row2024
3rd row2023/24
4th row2022/23
5th row2022

Common Values

ValueCountFrequency (%)
2024 83
 
9.2%
2022 72
 
7.9%
2023 65
 
7.2%
2023/24 61
 
6.7%
2021/22 57
 
6.3%
2022/23 57
 
6.3%
2019/20 53
 
5.8%
2019 52
 
5.7%
2021 40
 
4.4%
2018/19 26
 
2.9%
Other values (31) 341
37.6%

Length

2024-11-06T19:51:40.945071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024 83
 
9.2%
2022 72
 
7.9%
2023 65
 
7.2%
2023/24 61
 
6.7%
2021/22 57
 
6.3%
2022/23 57
 
6.3%
2019/20 53
 
5.8%
2019 52
 
5.7%
2021 40
 
4.4%
2018/19 26
 
2.9%
Other values (31) 341
37.6%

Most occurring characters

ValueCountFrequency (%)
2 1883
37.7%
0 1140
22.8%
1 636
 
12.7%
/ 456
 
9.1%
3 229
 
4.6%
4 202
 
4.0%
9 170
 
3.4%
8 86
 
1.7%
5 75
 
1.5%
7 60
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1883
37.7%
0 1140
22.8%
1 636
 
12.7%
/ 456
 
9.1%
3 229
 
4.6%
4 202
 
4.0%
9 170
 
3.4%
8 86
 
1.7%
5 75
 
1.5%
7 60
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1883
37.7%
0 1140
22.8%
1 636
 
12.7%
/ 456
 
9.1%
3 229
 
4.6%
4 202
 
4.0%
9 170
 
3.4%
8 86
 
1.7%
5 75
 
1.5%
7 60
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1883
37.7%
0 1140
22.8%
1 636
 
12.7%
/ 456
 
9.1%
3 229
 
4.6%
4 202
 
4.0%
9 170
 
3.4%
8 86
 
1.7%
5 75
 
1.5%
7 60
 
1.2%

AveRPW
Real number (ℝ)

HIGH CORRELATION 

Distinct737
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.092966
Minimum0
Maximum139
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:41.303443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.168
Q116.4
median21.18
Q325.95
95-th percentile36.539
Maximum139
Range139
Interquartile range (IQR)9.55

Descriptive statistics

Standard deviation9.8820316
Coefficient of variation (CV)0.44729312
Kurtosis36.247946
Mean22.092966
Median Absolute Deviation (MAD)4.78
Skewness3.7993743
Sum20038.32
Variance97.654549
MonotonicityNot monotonic
2024-11-06T19:51:41.716088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.3 6
 
0.7%
18 5
 
0.6%
18.76 4
 
0.4%
19.66 3
 
0.3%
24.28 3
 
0.3%
20 3
 
0.3%
18.5 3
 
0.3%
13.2 3
 
0.3%
29.2 3
 
0.3%
19.44 3
 
0.3%
Other values (727) 871
96.0%
ValueCountFrequency (%)
0 2
0.2%
3.35 1
0.1%
3.56 1
0.1%
4.04 1
0.1%
5 1
0.1%
5.31 1
0.1%
5.81 1
0.1%
6.05 1
0.1%
6.08 2
0.2%
6.15 1
0.1%
ValueCountFrequency (%)
139 1
0.1%
127 1
0.1%
75.75 1
0.1%
64.46 1
0.1%
58.33 1
0.1%
54.85 1
0.1%
51.5 1
0.1%
51.23 1
0.1%
50.83 1
0.1%
50.72 1
0.1%

AveRPO
Real number (ℝ)

HIGH CORRELATION 

Distinct421
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3587762
Minimum0
Maximum13.43
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-06T19:51:42.159536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.869
Q16.545
median7.5
Q38.25
95-th percentile9.405
Maximum13.43
Range13.43
Interquartile range (IQR)1.705

Descriptive statistics

Standard deviation1.4324488
Coefficient of variation (CV)0.19465857
Kurtosis1.6324941
Mean7.3587762
Median Absolute Deviation (MAD)0.82
Skewness-0.34967151
Sum6674.41
Variance2.0519096
MonotonicityNot monotonic
2024-11-06T19:51:42.603240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.28 9
 
1.0%
7.95 8
 
0.9%
8.51 7
 
0.8%
7.55 7
 
0.8%
8.27 7
 
0.8%
7.78 6
 
0.7%
7.72 6
 
0.7%
7.43 6
 
0.7%
6.83 6
 
0.7%
7.5 6
 
0.7%
Other values (411) 839
92.5%
ValueCountFrequency (%)
0 1
0.1%
2.52 1
0.1%
2.67 1
0.1%
3.04 1
0.1%
3.12 1
0.1%
3.13 1
0.1%
3.2 1
0.1%
3.3 1
0.1%
3.42 1
0.1%
3.48 1
0.1%
ValueCountFrequency (%)
13.43 1
0.1%
12.82 1
0.1%
12.54 1
0.1%
11.75 1
0.1%
11.15 1
0.1%
11.12 1
0.1%
11.05 1
0.1%
10.7 1
0.1%
10.63 1
0.1%
10.52 1
0.1%

Interactions

2024-11-06T19:51:27.879377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:50:58.972527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:02.238566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:05.874056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:09.660787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:13.134429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:16.593914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:19.937957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:23.920758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:28.219243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:50:59.368850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:02.601950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:06.241750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:10.004271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:13.505500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:16.962750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:20.321871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:24.301983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:28.610821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:50:59.709111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:03.028078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:06.702219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:10.376773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:13.857174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:17.351486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:20.713118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:24.797396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:28.951073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:00.019592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:03.439354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:07.057256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:10.790895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:14.284353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:17.712833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:21.090070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:25.259146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:29.252227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:00.449286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:03.855154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:07.432873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:11.212262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:14.614635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:18.121569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:21.525525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:25.726964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:29.534244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:00.779670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:04.236507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:07.843049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:11.562510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:14.990303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:18.486116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:21.920655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:26.164033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:29.870980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:01.152241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:04.604384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:08.207682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:11.966656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:15.384197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:18.859149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:22.230894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:26.661440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:30.238379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:01.507544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:05.045023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:08.633208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:12.390202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:15.795917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:19.211214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:22.705988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:27.113653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:30.585017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:01.850729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:05.462099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:09.047906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:12.752696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:16.180419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:19.629525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:23.189836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:51:27.501452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-11-06T19:51:42.925474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AveRPOAveRPWHSInnsLSLostMatNRSeasonTiedW/LWon
AveRPO1.0000.8400.7750.137-0.015-0.2710.1380.0280.1440.0000.5120.418
AveRPW0.8401.0000.6600.155-0.202-0.3280.1530.0000.1290.0000.6400.503
HS0.7750.6601.0000.4880.1010.0840.4850.0550.1350.0290.4650.601
Inns0.1370.1550.4881.0000.2740.6740.9970.1850.2060.1620.2700.771
LS-0.015-0.2020.1010.2741.0000.3340.2780.0720.0790.130-0.0980.117
Lost-0.271-0.3280.0840.6740.3341.0000.6710.0510.0670.063-0.4640.122
Mat0.1380.1530.4850.9970.2780.6711.0000.2240.1770.1580.2710.769
NR0.0280.0000.0550.1850.0720.0510.2241.0000.1500.0300.0490.118
Season0.1440.1290.1350.2060.0790.0670.1770.1501.0000.0000.0500.230
Tied0.0000.0000.0290.1620.1300.0630.1580.0300.0001.0000.0560.118
W/L0.5120.6400.4650.270-0.098-0.4640.2710.0490.0500.0561.0000.775
Won0.4180.5030.6010.7710.1170.1220.7690.1180.2300.1180.7751.000

Missing values

2024-11-06T19:51:31.048578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T19:51:31.670094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TeamMatWonLostTiedNRW/LInnsHSLSSeasonAveRPWAveRPO
0Zimbabwe550005.00000053440.02024/2543.6012.82
1Zimbabwe1028000.25000010159124.0202417.487.16
2Zimbabwe1789000.8888891721782.02023/2424.037.81
3Zimbabwe1155011.00000011174115.02022/2316.947.13
4Zimbabwe1697001.2857141623695.0202222.457.75
5Zimbabwe1468000.75000014193138.0202120.607.22
6Zimbabwe606000.0000006156148.02020/2119.387.28
7Zimbabwe633001.0000006177152.02019/2024.567.98
8Zimbabwe825100.4000008172136.0201922.018.23
9Zimbabwe202000.0000002132126.02018/1915.176.91
TeamMatWonLostTiedNRW/LInnsHSLSSeasonAveRPWAveRPO
897Afghanistan220002.00000021580.02017/1819.928.04
898Afghanistan303000.000000314693.0201713.426.52
899Afghanistan101000010.000000102330.02016/1733.728.92
900Afghanistan17125002.40000017215160.02015/1624.858.27
901Afghanistan651005.00000062100.0201531.968.37
902Afghanistan1046000.6666671017172.02013/1417.156.92
903Afghanistan422001.000000414080.02012/1316.266.56
904Afghanistan321002.00000031740.02011/1222.667.97
905Afghanistan202000.000000211580.0201010.835.41
906Afghanistan642002.00000061470.02009/1020.226.89